The increasing use of messaging applications such as WhatsApp for both social and personal purposes has determined an increase in the widespread use of these technologies, even in healthcare. A growing number of healthcare professionals have adopted WhatsApp in their daily work in order to share information with peers and patients. Past research has highlighted the advantages and disadvantages of WhatsApp usage in healthcare settings; in particular two positions appear to coexist in the scientific debate: those that expose and underline all of the positive aspects of the phenomenon, and those which also highlight the negative aspects, linked in particular to the clinical risks for patients, data security and privacy protection. The main objective of this study was to assess if and how individual and organizational determinants can trigger or inhibit the use of WhatsApp in a hospital setting, and which variables managers can exploit to guide professionals’ behaviors. Data were collected through a survey administered to physicians and nurses in an Italian University Hospital in Rome; a total of 191 high-quality responses were received. The results show that WhatsApp is widely used in the Hospital, and that its use is mainly due to the perception of numerous advantages and benefits reported in clinical practice. Moreover, an interplay exists between organizational and individual factors in determining the use of WhatsApp between healthcare professionals and with patients. In particular, individual factors play a key role as determinants of the use of WhatsApp; healthcare professionals use this technology mainly based on its perceived usefulness. Instead, organizational factors play a secondary role; they do not have a direct influence on the use of WhatsApp, but always act through individual factors. This study is the first to analyses the influence of individual and organizational determinants of WhatsApp usage in the hospital setting, and provides hospital managers with important information in order to manage this phenomenon and implement adequate strategies to exploit its potential increase.

The implementation of hospital-wide Electronic Medical Records (EMRs) is still an unsolved quest for many hospital managers. EMRs have long been considered a key factor for improving healthcare quality and safety, reducing adverse events for patients, decreasing costs, optimizing processes, improving clinical research and obtaining best clinical performances. However, hospitals continue to experience resistance from professionals to accepting EMRs. This study combines institutional and individual factors to explain which determinants can trigger or inhibit the EMRs implementation in hospitals, and which variables managers can exploit to guide professionals’ behaviours. Data have been collected through a survey administered to physicians and nurses in an Italian University Hospital in Rome. A total of 114 high-quality responses had been received. Results show that both, physicians and nurses, expect many benefits from the use of EMRs. In particular, it is believed that the EMRs will have a positive impact on quality, efficiency and effectiveness of care; handover communication between healthcare workers; teaching, tutoring and research activities; greater control of your own business. Moreover, data show an interplay between individual and institutional determinants: normative factors directly affect perceived usefulness (C = 0.30 **), perceived ease of use (C = 0.26 **) and intention to use EMRs (C = 0.33 **), regulative factors affect the intention to use EMRs (C = -0.21 **), and perceived usefulness directly affect the intention to use EMRs (C = 0.33 **). The analysis carried out shows that the key determinants of the intention to use EMRs are the normative ones (peer influence) and the individual ones (perceived usefulness), and that perceived usefulness works also as a mediator between normative factors and intention to use EMRs. Therefore, Management can leverage on power users to motivate, generate and manage change.

Introduction: Work contexts can affect nurses’ work and work outcomes. Work context factors of nurses, patients, or workflow can modulate nurses’ organization of work and determine increased workloads. Aim: The aim of this research was to analyze relationships between factors regarding the patient, the nurse, workflow, and nurses’ work organization, to investigate whether work organization is related to physical, mental, and emotional workloads, and to explore whether one dimension of workload influences the other dimensions. Methods: We used a cross-sectional design based on the Job Demand-Resources theory. We asked registered nurses, working in nine medical-surgical wards across three hospitals in Italy, to self-report on work organization and workloads regarding randomized shifts over three consecutive weeks. Four scales from the QEEW 2.0 questionnaire were used on an online survey for data collection. multivariable linear regressions with structural equation modelling were tested. The study was approved by the three local Ethics Committees. Results: We received 334 questionnaires regarding 125 shifts worked. Patient complexity (β = 0.347), patient specialties (β = 0.127), adequacy of staffing (β = −0.204), collaboration with colleagues (β = −0.155), unscheduled activities (β = 0.213), supply search (β = 0.141), and documentation (β = 0.221) significantly influenced nurses’ work organization. Nurses’ work organization was significantly related to physical, mental, and emotional nursing workloads. Conclusions: the patient, the nurse, and workflow aspects influence nurses’ work organization and workloads. Healthcare organizations, managers, and nurses should explore work settings to identify work turbulences early and implement strategies to improve nursing work conditions and workloads.

Background/Objectives: Artificial intelligence (AI) is revolutionizing the healthcare industry, improving diagnoses, treatments, and clinical processes. However, its integration poses significant cybersecurity risks, including data breaches, algorithmic opacity, and vulnerabilities in AI-controlled medical devices. This narrative review analyzes these threats and evaluates blockchain technology as a potential mitigation strategy within a Clinical Risk Management framework. Methods: The literature search was conducted on PubMed, Scopus, and Web of Science, considering peer-reviewed publications from 2000 to January 2025. 1,204 articles were identified. Inclusion criteria included studies on cybersecurity risks in healthcare, blockchain applications in the clinical setting, and regulatory references (eg, General Data Protection Regulation). Conference abstracts, non-English articles, and non-peer-reviewed contributions were excluded. To ensure methodological rigor, the Scale for the Assessment of Narrative Review Articles criteria were applied. Results: The thematic analysis highlighted recurring critical issues: difficulties with informed consent, unauthorized access to sensitive data, and systemic vulnerabilities in hospital digital infrastructures. Blockchain presents a promising solution thanks to its decentralization, immutability, and transparency. Integration with smart contracts enables dynamic consent management, secure data sharing, and real-time monitoring of medical devices. Permissioned networks improve traceability and regulatory compliance, while Layer 2 solutions and optimized consent protocols address scalability challenges. Conclusion: Despite its potential, blockchain adoption faces obstacles: high costs, regulatory rigidity, and poor acceptance among healthcare professionals. The review highlights the need for pilot projects, interdisciplinary collaboration, and regulatory updates for effective integration. Combining AI and blockchain in Clinical Risk Management can transform clinical risk management from reactive to proactive, improving patient safety, data governance, and accountability.

The COVID-19 pandemic raised concerns about the potential for co-infection or over-infection with other respiratory infections, as they can complicate the diagnosis, treatment and prognosis of the disease. This is also a challenge for forensic pathologists, who may come across cases where the presence of co-infection or over-infection is suspected or confirmed, and it is important that they take this into account when determining the cause of death. The aim of this systematic review is to analyse the prevalence of each specific pathogen co-infecting or over-infecting patients with SARS-CoV-2 infection. In total, 575 studies were selected from the Scopus and Pub-Med online databases and 8 studies were included in a meta-analysis. Male gender, advanced age and nursing home care are risk factors associated with the development of co-infection, whereas age, tachypnoea, hypoxaemia and bacterial infection are predictors of mortality. Overall, however, having a SARS-CoV-2 infection does not represent a real risk for the development of co-infections/super-infections.

The syndemic framework proposed by the 2021–2030 World Health Organization (WHO) action plan for patient safety and the introduction of enabling technologies in health services involve a more effective interpretation of the data to understand causation. Based on the Systemic Theory, this communication proposes the “Systemic Clinical Risk Management” (SCRM) to improve the Quality of Care and Patient Safety. This is a new Clinical Risk Management model capable of developing the ability to observe and synthesize different elements in ways that lead to in-depth interventions to achieve solutions aligned with the sustainable development of health services. In order to avoid uncontrolled decision-making related to the use of enabling technologies, we devised an internal Learning Algorithm Risk Management (LARM) level based on a Bayesian approach. Moreover, according to the ethics of Job Well Done, the SCRM, instead of giving an opinion on events that have already occurred, proposes a bioethical co-working because it suggests the best way to act from a scientific point of view.